SOTAVerified

Multi-agent Reinforcement Learning

The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.

Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports

Papers

Showing 211220 of 1718 papers

TitleStatusHype
Negative Update Intervals in Deep Multi-Agent Reinforcement LearningCode1
Neural Auto-Curricula in Two-Player Zero-Sum GamesCode1
Context-aware Communication for Multi-agent Reinforcement LearningCode1
Off-Policy Multi-Agent Decomposed Policy GradientsCode1
Controlling Behavioral Diversity in Multi-Agent Reinforcement LearningCode1
Optimal control towards sustainable wastewater treatment plants based on multi-agent reinforcement learningCode1
CAMMARL: Conformal Action Modeling in Multi Agent Reinforcement LearningCode1
CAMP: Collaborative Attention Model with Profiles for Vehicle Routing ProblemsCode1
Collaborating with Humans without Human DataCode1
CoLight: Learning Network-level Cooperation for Traffic Signal ControlCode1
Show:102550
← PrevPage 22 of 172Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MATD3final agent reward-14Unverified
#ModelMetricClaimedVerifiedStatus
1DRIMAMedian Win Rate15Unverified
#ModelMetricClaimedVerifiedStatus
1Fusion-Multi-Actor-Attention-CriticAverage Reward39Unverified